Comparing Dataset A and Dataset B

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	432	446
Missing cells (%)	8.1%	8.3%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Survived` is highly overall correlated with `Sex`	`Survived` is highly overall correlated with `Sex`	High Correlation
`Sex` is highly overall correlated with `Survived`	`Sex` is highly overall correlated with `Survived`	High Correlation
`Age` has 90 (20.2%) missing values	`Age` has 96 (21.5%) missing values	Missing
`Cabin` has 341 (76.5%) missing values	`Cabin` has 350 (78.5%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 308 (69.1%) zeros	`SibSp` has 307 (68.8%) zeros	Zeros
`Parch` has 347 (77.8%) zeros	`Parch` has 342 (76.7%) zeros	Zeros
`Fare` has 6 (1.3%) zeros	`Fare` has 10 (2.2%) zeros	Zeros

Reproduction

	Dataset A	Dataset B
Analysis started	2023-09-12 08:02:43.806318	2023-09-12 08:02:48.960807
Analysis finished	2023-09-12 08:02:48.958929	2023-09-12 08:02:53.110154
Duration	5.15 seconds	4.15 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	431.71076	451.92601

	Dataset A	Dataset B
Minimum	2	1
Maximum	887	891
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	2	1
5-th percentile	43.5	51.5
Q1	196.25	231.5
median	428	456.5
Q3	652.5	684.75
95-th percentile	828	853.75
Maximum	887	891
Range	885	890
Interquartile range (IQR)	456.25	453.25

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	255.28192	256.96998
Coefficient of variation (CV)	0.59132628	0.56861073
Kurtosis	-1.1982817	-1.1944421
Mean	431.71076	451.92601
Median Absolute Deviation (MAD)	229	227
Skewness	0.019860349	0.0072009949
Sum	192543	201559
Variance	65168.858	66033.57
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
147	1	0.2%
713	1	0.2%
777	1	0.2%
494	1	0.2%
849	1	0.2%
490	1	0.2%
410	1	0.2%
99	1	0.2%
192	1	0.2%
243	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
694	1	0.2%
355	1	0.2%
464	1	0.2%
11	1	0.2%
129	1	0.2%
555	1	0.2%
199	1	0.2%
184	1	0.2%
638	1	0.2%
8	1	0.2%
Other values (436)	436	97.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
2	1	0.2%
3	1	0.2%
4	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%
11	1	0.2%
15	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
6	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%
11	1	0.2%
13	1	0.2%
14	1	0.2%
18	1	0.2%
20	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
6	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%
11	1	0.2%
13	1	0.2%
14	1	0.2%
18	1	0.2%
20	1	0.2%

Value	Count	Frequency (%)
2	1	0.2%
3	1	0.2%
4	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%
11	1	0.2%
15	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	277
1	169

0	286
1	160

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	1	0
2nd row	0	0
3rd row	0	0
4th row	0	0
5th row	1	0

Common Values

Value	Count	Frequency (%)
0	277	62.1%
1	169	37.9%

Value	Count	Frequency (%)
0	286	64.1%
1	160	35.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	277	62.1%
1	169	37.9%

Value	Count	Frequency (%)
0	286	64.1%
1	160	35.9%

Most occurring characters

Value	Count	Frequency (%)
0	277	62.1%
1	169	37.9%

Value	Count	Frequency (%)
0	286	64.1%
1	160	35.9%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	277	62.1%
1	169	37.9%

Value	Count	Frequency (%)
0	286	64.1%
1	160	35.9%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	277	62.1%
1	169	37.9%

Value	Count	Frequency (%)
0	286	64.1%
1	160	35.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	277	62.1%
1	169	37.9%

Value	Count	Frequency (%)
0	286	64.1%
1	160	35.9%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	252
1	105
2	89

3	253
1	103
2	90

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	3	3
2nd row	3	3
3rd row	3	3
4th row	2	3
5th row	2	3

Common Values

Value	Count	Frequency (%)
3	252	56.5%
1	105	23.5%
2	89	20.0%

Value	Count	Frequency (%)
3	253	56.7%
1	103	23.1%
2	90	20.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	252	56.5%
1	105	23.5%
2	89	20.0%

Value	Count	Frequency (%)
3	253	56.7%
1	103	23.1%
2	90	20.2%

Most occurring characters

Value	Count	Frequency (%)
3	252	56.5%
1	105	23.5%
2	89	20.0%

Value	Count	Frequency (%)
3	253	56.7%
1	103	23.1%
2	90	20.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	252	56.5%
1	105	23.5%
2	89	20.0%

Value	Count	Frequency (%)
3	253	56.7%
1	103	23.1%
2	90	20.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	252	56.5%
1	105	23.5%
2	89	20.0%

Value	Count	Frequency (%)
3	253	56.7%
1	103	23.1%
2	90	20.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	252	56.5%
1	105	23.5%
2	89	20.0%

Value	Count	Frequency (%)
3	253	56.7%
1	103	23.1%
2	90	20.2%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	82	57
Median length	48	48
Mean length	27.013453	26.697309
Min length	15	12

Characters and Unicode

	Dataset A	Dataset B
Total characters	12048	11907
Distinct characters	60	59
Distinct categories	7	7 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Andersson, Mr. August Edvard ("Wennerstrom")	Saad, Mr. Khalil
2nd row	Coelho, Mr. Domingos Fernandeo	Boulos, Miss. Nourelain
3rd row	Gronnestad, Mr. Daniel Danielsen	Gallagher, Mr. Martin
4th row	Jenkin, Mr. Stephen Curnow	Sivic, Mr. Husein
5th row	Caldwell, Master. Alden Gates	Hampe, Mr. Leon

Value	Count	Frequency (%)
mr	256	14.1%
miss	84	4.6%
mrs	74	4.1%
william	29	1.6%
john	24	1.3%
henry	21	1.2%
master	20	1.1%
james	15	0.8%
charles	13	0.7%
thomas	12	0.7%
Other values (877)	1267	69.8%

Value	Count	Frequency (%)
mr	271	15.1%
miss	91	5.1%
mrs	58	3.2%
william	28	1.6%
master	20	1.1%
henry	19	1.1%
james	15	0.8%
john	14	0.8%
george	12	0.7%
joseph	10	0.6%
Other values (904)	1256	70.0%

Most occurring characters

Value	Count	Frequency (%)
	1370	11.4%
r	995	8.3%
e	865	7.2%
a	845	7.0%
s	653	5.4%
n	621	5.2%
i	620	5.1%
M	568	4.7%
l	549	4.6%
o	504	4.2%
Other values (50)	4458	37.0%

Value	Count	Frequency (%)
	1349	11.3%
r	976	8.2%
a	845	7.1%
e	832	7.0%
i	659	5.5%
s	649	5.5%
n	625	5.2%
M	572	4.8%
l	517	4.3%
o	484	4.1%
Other values (49)	4399	36.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7739	64.2%
Uppercase Letter	1829	15.2%
Space Separator	1370	11.4%
Other Punctuation	942	7.8%
Close Punctuation	80	0.7%
Open Punctuation	80	0.7%
Dash Punctuation	8	0.1%

Value	Count	Frequency (%)
Lowercase Letter	7653	64.3%
Uppercase Letter	1807	15.2%
Space Separator	1349	11.3%
Other Punctuation	959	8.1%
Close Punctuation	66	0.6%
Open Punctuation	66	0.6%
Dash Punctuation	7	0.1%

Most frequent character per category

Space Separator

Value	Count	Frequency (%)
	1370	100.0%

Value	Count	Frequency (%)
	1349	100.0%

Lowercase Letter

Value	Count	Frequency (%)
r	995	12.9%
e	865	11.2%
a	845	10.9%
s	653	8.4%
n	621	8.0%
i	620	8.0%
l	549	7.1%
o	504	6.5%
t	337	4.4%
h	263	3.4%
Other values (16)	1487	19.2%

Value	Count	Frequency (%)
r	976	12.8%
a	845	11.0%
e	832	10.9%
i	659	8.6%
s	649	8.5%
n	625	8.2%
l	517	6.8%
o	484	6.3%
t	335	4.4%
h	255	3.3%
Other values (16)	1476	19.3%

Uppercase Letter

Value	Count	Frequency (%)
M	568	31.1%
J	120	6.6%
A	115	6.3%
H	95	5.2%
S	90	4.9%
C	89	4.9%
E	84	4.6%
B	80	4.4%
W	72	3.9%
R	57	3.1%
Other values (15)	459	25.1%

Value	Count	Frequency (%)
M	572	31.7%
A	127	7.0%
H	108	6.0%
J	103	5.7%
S	88	4.9%
C	78	4.3%
E	72	4.0%
W	69	3.8%
B	69	3.8%
L	65	3.6%
Other values (15)	456	25.2%

Other Punctuation

Value	Count	Frequency (%)
.	447	47.5%
,	446	47.3%
"	44	4.7%
'	4	0.4%
/	1	0.1%

Value	Count	Frequency (%)
,	446	46.5%
.	446	46.5%
"	62	6.5%
'	5	0.5%

Close Punctuation

Value	Count	Frequency (%)
)	80	100.0%

Value	Count	Frequency (%)
)	66	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	80	100.0%

Value	Count	Frequency (%)
(	66	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	8	100.0%

Value	Count	Frequency (%)
-	7	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9568	79.4%
Common	2480	20.6%

Value	Count	Frequency (%)
Latin	9460	79.4%
Common	2447	20.6%

Most frequent character per script

Common

Value	Count	Frequency (%)
	1370	55.2%
.	447	18.0%
,	446	18.0%
)	80	3.2%
(	80	3.2%
"	44	1.8%
-	8	0.3%
'	4	0.2%
/	1	< 0.1%

Value	Count	Frequency (%)
	1349	55.1%
,	446	18.2%
.	446	18.2%
)	66	2.7%
(	66	2.7%
"	62	2.5%
-	7	0.3%
'	5	0.2%

Latin

Value	Count	Frequency (%)
r	995	10.4%
e	865	9.0%
a	845	8.8%
s	653	6.8%
n	621	6.5%
i	620	6.5%
M	568	5.9%
l	549	5.7%
o	504	5.3%
t	337	3.5%
Other values (41)	3011	31.5%

Value	Count	Frequency (%)
r	976	10.3%
a	845	8.9%
e	832	8.8%
i	659	7.0%
s	649	6.9%
n	625	6.6%
M	572	6.0%
l	517	5.5%
o	484	5.1%
t	335	3.5%
Other values (41)	2966	31.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	12048	100.0%

Value	Count	Frequency (%)
ASCII	11907	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1370	11.4%
r	995	8.3%
e	865	7.2%
a	845	7.0%
s	653	5.4%
n	621	5.2%
i	620	5.1%
M	568	4.7%
l	549	4.6%
o	504	4.2%
Other values (50)	4458	37.0%

Value	Count	Frequency (%)
	1349	11.3%
r	976	8.2%
a	845	7.1%
e	832	7.0%
i	659	5.5%
s	649	5.5%
n	625	5.2%
M	572	4.8%
l	517	4.3%
o	484	4.1%
Other values (49)	4399	36.9%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	285
female	161

male	297
female	149

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.7219731	4.6681614
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2106	2082
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	male	male
2nd row	male	female
3rd row	male	male
4th row	male	male
5th row	male	male

Common Values

Value	Count	Frequency (%)
male	285	63.9%
female	161	36.1%

Value	Count	Frequency (%)
male	297	66.6%
female	149	33.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	285	63.9%
female	161	36.1%

Value	Count	Frequency (%)
male	297	66.6%
female	149	33.4%

Most occurring characters

Value	Count	Frequency (%)
e	607	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	161	7.6%

Value	Count	Frequency (%)
e	595	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	149	7.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2106	100.0%

Value	Count	Frequency (%)
Lowercase Letter	2082	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	607	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	161	7.6%

Value	Count	Frequency (%)
e	595	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	149	7.2%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2106	100.0%

Value	Count	Frequency (%)
Latin	2082	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	607	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	161	7.6%

Value	Count	Frequency (%)
e	595	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	149	7.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2106	100.0%

Value	Count	Frequency (%)
ASCII	2082	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	607	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	161	7.6%

Value	Count	Frequency (%)
e	595	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	149	7.2%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	75	75
Distinct (%)	21.1%	21.4%
Missing	90	96
Missing (%)	20.2%	21.5%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	30.916433	30.117629

	Dataset A	Dataset B
Minimum	0.42	0.75
Maximum	80	71
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.42	0.75
5-th percentile	4	6
Q1	22	21
median	29	28
Q3	39	39
95-th percentile	58	55.275
Maximum	80	71
Range	79.58	70.25
Interquartile range (IQR)	17	18

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	15.135201	14.435373
Coefficient of variation (CV)	0.48955199	0.47929978
Kurtosis	0.26532849	-0.16521907
Mean	30.916433	30.117629
Median Absolute Deviation (MAD)	8	9
Skewness	0.43310607	0.29207307
Sum	11006.25	10541.17
Variance	229.07431	208.37999
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
22	18	4.0%
30	14	3.1%
36	13	2.9%
25	13	2.9%
28	13	2.9%
29	12	2.7%
18	12	2.7%
26	11	2.5%
21	11	2.5%
24	10	2.2%
Other values (65)	229	51.3%
(Missing)	90	20.2%

Value	Count	Frequency (%)
25	17	3.8%
28	14	3.1%
24	13	2.9%
27	13	2.9%
30	12	2.7%
35	10	2.2%
18	10	2.2%
22	10	2.2%
29	10	2.2%
34	9	2.0%
Other values (65)	232	52.0%
(Missing)	96	21.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.42	1	0.2%
0.83	1	0.2%
1	5	1.1%
2	7	1.6%
3	1	0.2%
4	4	0.9%
5	1	0.2%
6	2	0.4%
7	1	0.2%
8	2	0.4%

Value	Count	Frequency (%)
0.75	1	0.2%
0.92	1	0.2%
1	3	0.7%
2	5	1.1%
3	1	0.2%
4	4	0.9%
5	2	0.4%
6	2	0.4%
7	2	0.4%
8	2	0.4%

Value	Count	Frequency (%)
0.75	1	0.2%
0.92	1	0.2%
1	3	0.7%
2	5	1.1%
3	1	0.2%
4	4	0.9%
5	2	0.4%
6	2	0.4%
7	2	0.4%
8	2	0.4%

Value	Count	Frequency (%)
0.42	1	0.2%
0.83	1	0.2%
1	5	1.1%
2	7	1.6%
3	1	0.2%
4	4	0.9%
5	1	0.2%
6	2	0.4%
7	1	0.2%
8	2	0.4%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.49775785	0.49103139

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	308	307
Zeros (%)	69.1%	68.8%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	2.75	2
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.0721162	1.0116891
Coefficient of variation (CV)	2.1538911	2.0603349
Kurtosis	18.478005	16.776802
Mean	0.49775785	0.49103139
Median Absolute Deviation (MAD)	0	0
Skewness	3.7645961	3.5037685
Sum	222	219
Variance	1.1494332	1.0235149
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	308	69.1%
1	107	24.0%
4	9	2.0%
2	8	1.8%
3	8	1.8%
8	3	0.7%
5	3	0.7%

Value	Count	Frequency (%)
0	307	68.8%
1	105	23.5%
2	12	2.7%
4	10	2.2%
3	8	1.8%
8	2	0.4%
5	2	0.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	308	69.1%
1	107	24.0%
2	8	1.8%
3	8	1.8%
4	9	2.0%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	307	68.8%
1	105	23.5%
2	12	2.7%
3	8	1.8%
4	10	2.2%
5	2	0.4%
8	2	0.4%

Value	Count	Frequency (%)
0	307	68.8%
1	105	23.5%
2	12	2.7%
3	8	1.8%
4	10	2.2%
5	2	0.4%
8	2	0.4%

Value	Count	Frequency (%)
0	308	69.1%
1	107	24.0%
2	8	1.8%
3	8	1.8%
4	9	2.0%
5	3	0.7%
8	3	0.7%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	6	7
Distinct (%)	1.3%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.35874439	0.36995516

	Dataset A	Dataset B
Minimum	0	0
Maximum	5	6
Zeros	347	342
Zeros (%)	77.8%	76.7%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0	0
95-th percentile	2	2
Maximum	5	6
Range	5	6
Interquartile range (IQR)	0	0

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.79124549	0.82646859
Coefficient of variation (CV)	2.2055968	2.2339697
Kurtosis	9.6071043	12.524856
Mean	0.35874439	0.36995516
Median Absolute Deviation (MAD)	0	0
Skewness	2.7958988	3.1311993
Sum	160	165
Variance	0.62606943	0.68305034
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	347	77.8%
1	54	12.1%
2	37	8.3%
5	3	0.7%
3	3	0.7%
4	2	0.4%

Value	Count	Frequency (%)
0	342	76.7%
1	65	14.6%
2	28	6.3%
3	5	1.1%
5	3	0.7%
4	2	0.4%
6	1	0.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	347	77.8%
1	54	12.1%
2	37	8.3%
3	3	0.7%
4	2	0.4%
5	3	0.7%

Value	Count	Frequency (%)
0	342	76.7%
1	65	14.6%
2	28	6.3%
3	5	1.1%
4	2	0.4%
5	3	0.7%
6	1	0.2%

Value	Count	Frequency (%)
0	342	76.7%
1	65	14.6%
2	28	6.3%
3	5	1.1%
4	2	0.4%
5	3	0.7%
6	1	0.2%

Value	Count	Frequency (%)
0	347	77.8%
1	54	12.1%
2	37	8.3%
3	3	0.7%
4	2	0.4%
5	3	0.7%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	386	378
Distinct (%)	86.5%	84.8%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	17	17
Mean length	6.6883408	6.793722
Min length	3	3

Characters and Unicode

	Dataset A	Dataset B
Total characters	2983	3030
Distinct characters	32	32
Distinct categories	5	5 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	333	326 ?
Unique (%)	74.7%	73.1%

Sample

	Dataset A	Dataset B
1st row	350043	2672
2nd row	SOTON/O.Q. 3101307	2678
3rd row	8471	36864
4th row	C.A. 33111	349251
5th row	248738	345769

Value	Count	Frequency (%)
pc	36	6.3%
c.a	15	2.6%
a/5	8	1.4%
ca	7	1.2%
2	5	0.9%
ston/o	5	0.9%
382652	4	0.7%
f.c.c	4	0.7%
w./c	4	0.7%
a/4	3	0.5%
Other values (404)	477	84.0%

Value	Count	Frequency (%)
pc	32	5.7%
a/5	10	1.8%
c.a	9	1.6%
soton/o.q	6	1.1%
ston/o	6	1.1%
2	6	1.1%
ca	6	1.1%
w./c	5	0.9%
ston/o2	5	0.9%
3101295	4	0.7%
Other values (396)	476	84.2%

Most occurring characters

Value	Count	Frequency (%)
3	379	12.7%
1	314	10.5%
2	291	9.8%
7	267	9.0%
4	236	7.9%
6	205	6.9%
5	195	6.5%
0	188	6.3%
9	165	5.5%
8	146	4.9%
Other values (22)	597	20.0%

Value	Count	Frequency (%)
3	376	12.4%
1	336	11.1%
2	292	9.6%
7	267	8.8%
4	250	8.3%
0	207	6.8%
6	189	6.2%
9	185	6.1%
5	179	5.9%
8	142	4.7%
Other values (22)	607	20.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2386	80.0%
Uppercase Letter	328	11.0%
Other Punctuation	142	4.8%
Space Separator	122	4.1%
Lowercase Letter	5	0.2%

Value	Count	Frequency (%)
Decimal Number	2423	80.0%
Uppercase Letter	337	11.1%
Other Punctuation	143	4.7%
Space Separator	119	3.9%
Lowercase Letter	8	0.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	379	15.9%
1	314	13.2%
2	291	12.2%
7	267	11.2%
4	236	9.9%
6	205	8.6%
5	195	8.2%
0	188	7.9%
9	165	6.9%
8	146	6.1%

Value	Count	Frequency (%)
3	376	15.5%
1	336	13.9%
2	292	12.1%
7	267	11.0%
4	250	10.3%
0	207	8.5%
6	189	7.8%
9	185	7.6%
5	179	7.4%
8	142	5.9%

Space Separator

Value	Count	Frequency (%)
	122	100.0%

Value	Count	Frequency (%)
	119	100.0%

Other Punctuation

Value	Count	Frequency (%)
.	98	69.0%
/	44	31.0%

Value	Count	Frequency (%)
.	91	63.6%
/	52	36.4%

Uppercase Letter

Value	Count	Frequency (%)
C	82	25.0%
P	60	18.3%
O	45	13.7%
A	39	11.9%
S	31	9.5%
N	19	5.8%
T	16	4.9%
W	7	2.1%
Q	6	1.8%
I	6	1.8%
Other values (5)	17	5.2%

Value	Count	Frequency (%)
C	72	21.4%
O	60	17.8%
P	47	13.9%
A	39	11.6%
S	38	11.3%
N	25	7.4%
T	23	6.8%
Q	10	3.0%
W	5	1.5%
I	5	1.5%
Other values (5)	13	3.9%

Lowercase Letter

Value	Count	Frequency (%)
a	2	40.0%
r	1	20.0%
i	1	20.0%
s	1	20.0%

Value	Count	Frequency (%)
a	2	25.0%
r	2	25.0%
i	2	25.0%
s	2	25.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	2650	88.8%
Latin	333	11.2%

Value	Count	Frequency (%)
Common	2685	88.6%
Latin	345	11.4%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	379	14.3%
1	314	11.8%
2	291	11.0%
7	267	10.1%
4	236	8.9%
6	205	7.7%
5	195	7.4%
0	188	7.1%
9	165	6.2%
8	146	5.5%
Other values (3)	264	10.0%

Value	Count	Frequency (%)
3	376	14.0%
1	336	12.5%
2	292	10.9%
7	267	9.9%
4	250	9.3%
0	207	7.7%
6	189	7.0%
9	185	6.9%
5	179	6.7%
8	142	5.3%
Other values (3)	262	9.8%

Latin

Value	Count	Frequency (%)
C	82	24.6%
P	60	18.0%
O	45	13.5%
A	39	11.7%
S	31	9.3%
N	19	5.7%
T	16	4.8%
W	7	2.1%
Q	6	1.8%
I	6	1.8%
Other values (9)	22	6.6%

Value	Count	Frequency (%)
C	72	20.9%
O	60	17.4%
P	47	13.6%
A	39	11.3%
S	38	11.0%
N	25	7.2%
T	23	6.7%
Q	10	2.9%
W	5	1.4%
I	5	1.4%
Other values (9)	21	6.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2983	100.0%

Value	Count	Frequency (%)
ASCII	3030	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	379	12.7%
1	314	10.5%
2	291	9.8%
7	267	9.0%
4	236	7.9%
6	205	6.9%
5	195	6.5%
0	188	6.3%
9	165	5.5%
8	146	4.9%
Other values (22)	597	20.0%

Value	Count	Frequency (%)
3	376	12.4%
1	336	11.1%
2	292	9.6%
7	267	8.8%
4	250	8.3%
0	207	6.8%
6	189	6.2%
9	185	6.1%
5	179	5.9%
8	142	4.7%
Other values (22)	607	20.0%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	182	181
Distinct (%)	40.8%	40.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	30.337481	31.170748

	Dataset A	Dataset B
Minimum	0	0
Maximum	512.3292	512.3292
Zeros	6	10
Zeros (%)	1.3%	2.2%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.225	7.05105
Q1	7.8958	7.8958
median	13.25	13.5
Q3	29.55625	30
95-th percentile	90	109.76873
Maximum	512.3292	512.3292
Range	512.3292	512.3292
Interquartile range (IQR)	21.66045	22.1042

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	49.259648	51.031205
Coefficient of variation (CV)	1.6237224	1.6371505
Kurtosis	44.351288	38.185896
Mean	30.337481	31.170748
Median Absolute Deviation (MAD)	6	6.275
Skewness	5.6248305	5.2129469
Sum	13530.516	13902.154
Variance	2426.5129	2604.1839
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
13	19	4.3%
7.8958	19	4.3%
8.05	18	4.0%
7.75	17	3.8%
10.5	13	2.9%
26	13	2.9%
7.2292	10	2.2%
7.8542	9	2.0%
8.6625	9	2.0%
7.925	8	1.8%
Other values (172)	311	69.7%

Value	Count	Frequency (%)
7.8958	26	5.8%
8.05	23	5.2%
13	21	4.7%
26	17	3.8%
7.75	15	3.4%
0	10	2.2%
7.225	9	2.0%
7.925	9	2.0%
10.5	9	2.0%
26.55	9	2.0%
Other values (171)	298	66.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	6	1.3%
4.0125	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.975	1	0.2%
7.0458	1	0.2%
7.05	4	0.9%
7.0542	1	0.2%
7.125	3	0.7%
7.225	8	1.8%

Value	Count	Frequency (%)
0	10	2.2%
4.0125	1	0.2%
5	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.975	2	0.4%
7.0458	1	0.2%
7.05	5	1.1%
7.0542	2	0.4%

Value	Count	Frequency (%)
0	10	2.2%
4.0125	1	0.2%
5	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.975	2	0.4%
7.0458	1	0.2%
7.05	5	1.1%
7.0542	2	0.4%

Value	Count	Frequency (%)
0	6	1.3%
4.0125	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.975	1	0.2%
7.0458	1	0.2%
7.05	4	0.9%
7.0542	1	0.2%
7.125	3	0.7%
7.225	8	1.8%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	83	85
Distinct (%)	79.0%	88.5%
Missing	341	350
Missing (%)	76.5%	78.5%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	15	11
Median length	3	3
Mean length	3.4761905	3.4375
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	365	330
Distinct characters	19	19
Distinct categories	3	3 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	63	74 ?
Unique (%)	60.0%	77.1%

Sample

	Dataset A	Dataset B
1st row	C78	C22 C26
2nd row	D17	B86
3rd row	D33	B5
4th row	C93	E38
5th row	C87	E24

Value	Count	Frequency (%)
g6	4	3.3%
f	3	2.5%
c65	2	1.7%
b22	2	1.7%
d17	2	1.7%
e101	2	1.7%
e121	2	1.7%
d33	2	1.7%
c93	2	1.7%
b98	2	1.7%
Other values (84)	98	81.0%

Value	Count	Frequency (%)
c83	2	1.8%
c52	2	1.8%
f	2	1.8%
f2	2	1.8%
e101	2	1.8%
b5	2	1.8%
e121	2	1.8%
g6	2	1.8%
d26	2	1.8%
c26	2	1.8%
Other values (86)	89	81.7%

Most occurring characters

Value	Count	Frequency (%)
3	37	10.1%
2	36	9.9%
B	32	8.8%
C	32	8.8%
6	26	7.1%
1	25	6.8%
5	24	6.6%
8	21	5.8%
E	19	5.2%
4	18	4.9%
Other values (9)	95	26.0%

Value	Count	Frequency (%)
2	36	10.9%
C	35	10.6%
1	29	8.8%
B	26	7.9%
6	23	7.0%
5	21	6.4%
3	19	5.8%
E	18	5.5%
8	18	5.5%
4	17	5.2%
Other values (9)	88	26.7%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	228	62.5%
Uppercase Letter	121	33.2%
Space Separator	16	4.4%

Value	Count	Frequency (%)
Decimal Number	208	63.0%
Uppercase Letter	109	33.0%
Space Separator	13	3.9%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	37	16.2%
2	36	15.8%
6	26	11.4%
1	25	11.0%
5	24	10.5%
8	21	9.2%
4	18	7.9%
9	17	7.5%
7	14	6.1%
0	10	4.4%

Value	Count	Frequency (%)
2	36	17.3%
1	29	13.9%
6	23	11.1%
5	21	10.1%
3	19	9.1%
8	18	8.7%
4	17	8.2%
0	17	8.2%
9	16	7.7%
7	12	5.8%

Uppercase Letter

Value	Count	Frequency (%)
B	32	26.4%
C	32	26.4%
E	19	15.7%
D	17	14.0%
F	7	5.8%
A	7	5.8%
G	6	5.0%
T	1	0.8%

Value	Count	Frequency (%)
C	35	32.1%
B	26	23.9%
E	18	16.5%
D	16	14.7%
F	5	4.6%
A	5	4.6%
G	3	2.8%
T	1	0.9%

Space Separator

Value	Count	Frequency (%)
	16	100.0%

Value	Count	Frequency (%)
	13	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	244	66.8%
Latin	121	33.2%

Value	Count	Frequency (%)
Common	221	67.0%
Latin	109	33.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	37	15.2%
2	36	14.8%
6	26	10.7%
1	25	10.2%
5	24	9.8%
8	21	8.6%
4	18	7.4%
9	17	7.0%
	16	6.6%
7	14	5.7%

Value	Count	Frequency (%)
2	36	16.3%
1	29	13.1%
6	23	10.4%
5	21	9.5%
3	19	8.6%
8	18	8.1%
4	17	7.7%
0	17	7.7%
9	16	7.2%
	13	5.9%

Latin

Value	Count	Frequency (%)
B	32	26.4%
C	32	26.4%
E	19	15.7%
D	17	14.0%
F	7	5.8%
A	7	5.8%
G	6	5.0%
T	1	0.8%

Value	Count	Frequency (%)
C	35	32.1%
B	26	23.9%
E	18	16.5%
D	16	14.7%
F	5	4.6%
A	5	4.6%
G	3	2.8%
T	1	0.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	365	100.0%

Value	Count	Frequency (%)
ASCII	330	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	37	10.1%
2	36	9.9%
B	32	8.8%
C	32	8.8%
6	26	7.1%
1	25	6.8%
5	24	6.6%
8	21	5.8%
E	19	5.2%
4	18	4.9%
Other values (9)	95	26.0%

Value	Count	Frequency (%)
2	36	10.9%
C	35	10.6%
1	29	8.8%
B	26	7.9%
6	23	7.0%
5	21	6.4%
3	19	5.8%
E	18	5.5%
8	18	5.5%
4	17	5.2%
Other values (9)	88	26.7%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	1	0
Missing (%)	0.2%	0.0%
Memory size	7.0 KiB	7.0 KiB

S	311
C	90
Q	44

S	322
C	85
Q	39

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	445	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	S	C
2nd row	S	C
3rd row	S	Q
4th row	S	S
5th row	S	S

Common Values

Value	Count	Frequency (%)
S	311	69.7%
C	90	20.2%
Q	44	9.9%
(Missing)	1	0.2%

Value	Count	Frequency (%)
S	322	72.2%
C	85	19.1%
Q	39	8.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	311	69.9%
c	90	20.2%
q	44	9.9%

Value	Count	Frequency (%)
s	322	72.2%
c	85	19.1%
q	39	8.7%

Most occurring characters

Value	Count	Frequency (%)
S	311	69.9%
C	90	20.2%
Q	44	9.9%

Value	Count	Frequency (%)
S	322	72.2%
C	85	19.1%
Q	39	8.7%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	445	100.0%

Value	Count	Frequency (%)
Uppercase Letter	446	100.0%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
S	311	69.9%
C	90	20.2%
Q	44	9.9%

Value	Count	Frequency (%)
S	322	72.2%
C	85	19.1%
Q	39	8.7%

Most occurring scripts

Value	Count	Frequency (%)
Latin	445	100.0%

Value	Count	Frequency (%)
Latin	446	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
S	311	69.9%
C	90	20.2%
Q	44	9.9%

Value	Count	Frequency (%)
S	322	72.2%
C	85	19.1%
Q	39	8.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	445	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
S	311	69.9%
C	90	20.2%
Q	44	9.9%

Value	Count	Frequency (%)
S	322	72.2%
C	85	19.1%
Q	39	8.7%

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Auto

Heatmap
Table

Dataset A

Dataset B

Dataset A

	PassengerId	Age	SibSp	Parch	Fare	Survived	Pclass	Sex	Embarked
PassengerId	1.000	0.097	-0.043	0.025	0.028	0.105	0.000	0.072	0.000
Age	0.097	1.000	-0.186	-0.239	0.161	0.146	0.261	0.000	0.111
SibSp	-0.043	-0.186	1.000	0.455	0.459	0.198	0.133	0.223	0.096
Parch	0.025	-0.239	0.455	1.000	0.385	0.099	0.000	0.253	0.041
Fare	0.028	0.161	0.459	0.385	1.000	0.342	0.495	0.227	0.172
Survived	0.105	0.146	0.198	0.099	0.342	1.000	0.371	0.542	0.109
Pclass	0.000	0.261	0.133	0.000	0.495	0.371	1.000	0.115	0.256
Sex	0.072	0.000	0.223	0.253	0.227	0.542	0.115	1.000	0.040
Embarked	0.000	0.111	0.096	0.041	0.172	0.109	0.256	0.040	1.000

Dataset B

	PassengerId	Age	SibSp	Parch	Fare	Survived	Pclass	Sex	Embarked
PassengerId	1.000	0.034	-0.084	-0.018	-0.037	0.013	0.000	0.034	0.000
Age	0.034	1.000	-0.265	-0.276	0.134	0.151	0.301	0.000	0.089
SibSp	-0.084	-0.265	1.000	0.448	0.405	0.117	0.096	0.164	0.000
Parch	-0.018	-0.276	0.448	1.000	0.419	0.120	0.000	0.225	0.000
Fare	-0.037	0.134	0.405	0.419	1.000	0.274	0.471	0.157	0.190
Survived	0.013	0.151	0.117	0.120	0.274	1.000	0.274	0.514	0.161
Pclass	0.000	0.301	0.096	0.000	0.471	0.274	1.000	0.000	0.244
Sex	0.034	0.000	0.164	0.225	0.157	0.514	0.000	1.000	0.104
Embarked	0.000	0.089	0.000	0.000	0.190	0.161	0.244	0.104	1.000

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset A

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset B

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
146	147	1	3	Andersson, Mr. August Edvard ("Wennerstrom")	male	27.00	0	0	350043	7.7958	NaN	S
131	132	0	3	Coelho, Mr. Domingos Fernandeo	male	20.00	0	0	SOTON/O.Q. 3101307	7.0500	NaN	S
769	770	0	3	Gronnestad, Mr. Daniel Danielsen	male	32.00	0	0	8471	8.3625	NaN	S
70	71	0	2	Jenkin, Mr. Stephen Curnow	male	32.00	0	0	C.A. 33111	10.5000	NaN	S
78	79	1	2	Caldwell, Master. Alden Gates	male	0.83	0	2	248738	29.0000	NaN	S
428	429	0	3	Flynn, Mr. James	male	NaN	0	0	364851	7.7500	NaN	Q
322	323	1	2	Slayter, Miss. Hilda Mary	female	30.00	0	0	234818	12.3500	NaN	Q
432	433	1	2	Louch, Mrs. Charles Alexander (Alice Adelaide Slow)	female	42.00	1	0	SC/AH 3085	26.0000	NaN	S
784	785	0	3	Ali, Mr. William	male	25.00	0	0	SOTON/O.Q. 3101312	7.0500	NaN	S
412	413	1	1	Minahan, Miss. Daisy E	female	33.00	1	0	19928	90.0000	C78	Q

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
693	694	0	3	Saad, Mr. Khalil	male	25.0	0	0	2672	7.2250	NaN	C
852	853	0	3	Boulos, Miss. Nourelain	female	9.0	1	1	2678	15.2458	NaN	C
703	704	0	3	Gallagher, Mr. Martin	male	25.0	0	0	36864	7.7417	NaN	Q
561	562	0	3	Sivic, Mr. Husein	male	40.0	0	0	349251	7.8958	NaN	S
441	442	0	3	Hampe, Mr. Leon	male	20.0	0	0	345769	9.5000	NaN	S
818	819	0	3	Holm, Mr. John Fredrik Alexander	male	43.0	0	0	C 7075	6.4500	NaN	S
797	798	1	3	Osman, Mrs. Mara	female	31.0	0	0	349244	8.6833	NaN	S
38	39	0	3	Vander Planke, Miss. Augusta Maria	female	18.0	2	0	345764	18.0000	NaN	S
357	358	0	2	Funk, Miss. Annie Clemmer	female	38.0	0	0	237671	13.0000	NaN	S
595	596	0	3	Van Impe, Mr. Jean Baptiste	male	36.0	1	1	345773	24.1500	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
188	189	0	3	Bourke, Mr. John	male	40.0	1	1	364849	15.5000	NaN	Q
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
222	223	0	3	Green, Mr. George Henry	male	51.0	0	0	21440	8.0500	NaN	S
254	255	0	3	Rosblom, Mrs. Viktor (Helena Wilhelmina)	female	41.0	0	2	370129	20.2125	NaN	S
317	318	0	2	Moraweck, Dr. Ernest	male	54.0	0	0	29011	14.0000	NaN	S
112	113	0	3	Barton, Mr. David John	male	22.0	0	0	324669	8.0500	NaN	S
347	348	1	3	Davison, Mrs. Thomas Henry (Mary E Finck)	female	NaN	1	0	386525	16.1000	NaN	S
711	712	0	1	Klaber, Mr. Herman	male	NaN	0	0	113028	26.5500	C124	S
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
58	59	1	2	West, Miss. Constance Mirium	female	5.0	1	2	C.A. 34651	27.7500	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
465	466	0	3	Goncalves, Mr. Manuel Estanslas	male	38.0	0	0	SOTON/O.Q. 3101306	7.0500	NaN	S
251	252	0	3	Strom, Mrs. Wilhelm (Elna Matilda Persson)	female	29.0	1	1	347054	10.4625	G6	S
563	564	0	3	Simmons, Mr. John	male	NaN	0	0	SOTON/OQ 392082	8.0500	NaN	S
476	477	0	2	Renouf, Mr. Peter Henry	male	34.0	1	0	31027	21.0000	NaN	S
550	551	1	1	Thayer, Mr. John Borland Jr	male	17.0	0	2	17421	110.8833	C70	C
537	538	1	1	LeRoy, Miss. Bertha	female	30.0	0	0	PC 17761	106.4250	NaN	C
557	558	0	1	Robbins, Mr. Victor	male	NaN	0	0	PC 17757	227.5250	NaN	C
492	493	0	1	Molson, Mr. Harry Markland	male	55.0	0	0	113787	30.5000	C30	S
791	792	0	2	Gaskell, Mr. Alfred	male	16.0	0	0	239865	26.0000	NaN	S
309	310	1	1	Francatelli, Miss. Laura Mabel	female	30.0	0	0	PC 17485	56.9292	E36	C

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Space Separator

Lowercase Letter

Uppercase Letter

Other Punctuation

Close Punctuation

Open Punctuation

Dash Punctuation

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Space Separator

Other Punctuation

Uppercase Letter

Lowercase Letter

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters